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Researchers used blood metabolite data from 18,476 people aged 1 to 96 to develop measures of metabolomic aging and age-related mortality risk. The measures were associated with chronic disease and death, and modestly improved risk discrimination beyond some existing assessments; the observational study does not show that lifestyle changes cause biological aging to slow.
Researchers analyzed blood metabolite profiles from 18,476 people aged 1 to 96 and developed two measures intended to capture aspects of biological aging and mortality risk. In a study published in Science Translational Medicine, the measures were associated with chronic disease and death, suggesting that blood chemistry could complement—but does not replace—current health assessments.
The research drew on participants in the Shanghai Pudong Cohort, recruited across six centers. The team used plasma samples and nano-assisted laser desorption/ionization mass spectrometry to measure metabolic features. A group of 5,252 healthy participants was used to build a metabolomic age model; researchers then tested it in 5,456 healthy participants and examined a separate disease cohort of 7,768 people. The groups’ mean ages were 55, 55 and 68, respectively.
For the healthy group, an ensemble model estimated metabolomic age. The researchers calculated metabolomic age acceleration, or mAA, as the difference between that estimate and a participant’s actual age. For participants with existing conditions, they created an age-deviation measure based on how metabolite-related mortality risk differed from what was expected for a person’s age. Baseline assessments began in 2019, chronic disease status was reassessed in 2023, and deaths were tracked through January 2025.
A panel of 56 age-related metabolites retained most of the age-predictive information in the full set of 232 features. Higher mAA was associated with greater risk of developing conditions including diabetes, hypertension and fatty liver disease, as well as higher risk of death from any cause. The measures also added to the ability to distinguish mortality risk beyond frailty assessments, cardiovascular health scores and routine blood biomarkers, although the reported improvement was modest.
What Blood-Based Age Measures Add
The findings point to a possible way of identifying differences in health risk that a person’s calendar age alone may not capture. If validated in further research, metabolomic measures could help researchers and clinicians assess patterns linked to multiple conditions, rather than relying on age or one routine marker in isolation.
That possibility remains prospective. The study found associations and reported modest improvements in risk discrimination; it did not establish that the measures are ready for clinical use, nor that changing a person’s metabolite profile would reduce disease or extend life. The results support investigation of blood-based profiling as a potential addition to population-level risk assessment, not as a stand-alone diagnosis.
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How the Cohort Was Followed
The study examined people across a wide age range and included both healthy participants and people with existing disease. Participants’ health information included questionnaires on diet, lifestyle, sleep, mental health and family medical history where available. In 2023, thousands completed repeat surveys and updated chronic disease assessments; 1,500 participants also had repeat plasma metabolomic profiling.
The researchers examined whether the modeling approach could be retrained using other kinds of data, including UK Biobank proteomic data, U.S. National Health and Nutrition Examination Survey physiological biomarkers, and human gut microbiome data. This was a test of the approach across data types, not evidence that the Shanghai results have been confirmed as a clinical tool in those populations.
Metabolites associated with aging implicated pathways involving threonine, serine, glycine, butanoate, cysteine and methionine. The report also identified individual metabolites associated with aging patterns, but such associations do not by themselves explain the biological causes of aging or establish how a person’s health will change.
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Limits of the Aging Signals
The results are observational, so they cannot show that particular foods, exercise or other exposures caused a change in biological aging. Higher mAA was associated with active and secondhand smoking, shift-work history, hair dye use and parental history of premature cardiovascular disease. It was also associated with greater reported consumption of several foods, while mixed grains and exercise were linked to lower values. These patterns may reflect other differences between participants and should not be read as proof of cause and effect.
Repeated analyses linked increased physical activity and reduced fried-food intake with more favorable mAA changes. Among participants with hypertension, those whose blood pressure was below 140/90 mmHg in 2023 had more favorable changes in the mortality-linked age-deviation measure than those whose hypertension remained uncontrolled. The study does not establish that these changes resulted from the behaviors or blood-pressure status described.
It is also not clear from the report whether these measures would perform equally well in populations outside the Shanghai cohort, what threshold should prompt action, or whether using them would improve health outcomes. The reported gains in distinguishing risk were modest, and the findings do not establish the measures as diagnostic tests.
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Further Validation Before Clinical Use
The study’s follow-up provides a basis for further testing, but the report does not identify a scheduled next trial or a date for clinical adoption. Researchers would need to assess the measures in additional populations and determine whether they add useful information in real-world care, beyond existing assessments and blood tests.
For now, the clearest next step is further research on whether metabolomic profiles can reliably stratify risk across different groups and whether acting on those results changes outcomes. The current findings describe links between blood chemistry, aging measures and health risk; they do not provide an individual diagnosis or a reason to change treatment without advice from a qualified health professional.
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Key Questions
What did the researchers measure?
They analyzed plasma metabolites, chemicals in blood involved in bodily processes, and used the results to estimate metabolomic age or age-related mortality risk.
Does a higher metabolomic age mean someone will develop a disease?
No. Higher metabolomic age acceleration was associated with greater risk in the study, but it does not predict with certainty what will happen to an individual or serve as a diagnosis.
Did the study prove that diet or exercise slows aging?
No. The reported links between lifestyle factors and metabolomic measures were observational. They do not establish that a particular food or behavior caused the measured differences.
Can these blood measures be used in routine care now?
The report describes them as having potential for future risk assessment. It does not establish them as validated routine clinical tests or specify that patients should seek testing.
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